
import pandas as pd
import numpy as np
import seaborn as sns
import os
import matplotlib.pyplot as plt


# import scaled average temp data
climate = pd.read_csv(r'C:\Users\kacie\Box\Personal\CareerFoundry\Machine Learning\Climate Data\Avg Temp w Date.csv', index_col = False)


# import full scaled weather data
full = pd.read_csv(r'C:\Users\kacie\Box\Personal\CareerFoundry\Machine Learning\Climate Data\Scaled Climate Data.csv', index_col = False)


# import pleasantness data 
predicts = pd.read_csv(r'C:\Users\kacie\Box\Personal\CareerFoundry\Machine Learning\Exercise 1.4\Pleasant Weather.csv', index_col = False)


climate.head()


# list climate table columns to compare to pleasant table columns
climate.columns.to_list()

['BASEL_temp_mean',
 'BELGRADE_temp_mean',
 'BUDAPEST_temp_mean',
 'DEBILT_temp_mean',
 'DUSSELDORF_temp_mean',
 'GDANSK_temp_mean',
 'HEATHROW_temp_mean',
 'KASSEL_temp_mean',
 'LJUBLJANA_temp_mean',
 'MAASTRICHT_temp_mean',
 'MADRID_temp_mean',
 'MUNCHENB_temp_mean',
 'OSLO_temp_mean',
 'ROMA_temp_mean',
 'SONNBLICK_temp_mean',
 'STOCKHOLM_temp_mean',
 'TOURS_temp_mean',
 'VALENTIA_temp_mean',
 'Date']


predicts.head()


# List pleasant table columns to compare to climate table columns
predicts.columns.to_list()

['DATE',
 'BASEL_pleasant_weather',
 'BELGRADE_pleasant_weather',
 'BUDAPEST_pleasant_weather',
 'DEBILT_pleasant_weather',
 'DUSSELDORF_pleasant_weather',
 'HEATHROW_pleasant_weather',
 'KASSEL_pleasant_weather',
 'LJUBLJANA_pleasant_weather',
 'MAASTRICHT_pleasant_weather',
 'MADRID_pleasant_weather',
 'MUNCHENB_pleasant_weather',
 'OSLO_pleasant_weather',
 'SONNBLICK_pleasant_weather',
 'STOCKHOLM_pleasant_weather',
 'VALENTIA_pleasant_weather']


# Drop locations from climate table that are not in pleasant table
climate_small = climate.drop(['GDANSK_temp_mean', 'ROMA_temp_mean', 'TOURS_temp_mean'], axis = 1)
climate_small.shape

(22950, 16)


predicts.shape

(22950, 16)


# drop dates from temp and pleasant tables
temp = climate_small.drop(['Date'], axis = 1)
pleasant = predicts.drop(['DATE'], axis = 1)
print(pleasant.shape)
print(temp.shape)

(22950, 15)
(22950, 15)


# import libraries for KNN analysis
from sklearn.model_selection import train_test_split
from sklearn.metrics import multilabel_confusion_matrix, accuracy_score, confusion_matrix, ConfusionMatrixDisplay
from sklearn.neighbors import KNeighborsClassifier


# split data into testing and training sets
X = temp
y = pleasant
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=5)


from sklearn.multioutput import MultiOutputClassifier

# train and fit KNN model
k_range = np.arange(1,15)
train_acc = np.empty(len(k_range))
test_acc = np.empty(len(k_range))
scores = {}
scores_list = []
for i, k in enumerate(k_range):
    print("i -", i)
    knn = KNeighborsClassifier(n_neighbors=k)
    classifier = MultiOutputClassifier(knn, n_jobs=-1)
    knn.fit(X_train, y_train)
    y_pred = knn.predict(X_test)
    train_acc[i] = knn.score(X_train, np.array(y_train))
    test_acc[i] = knn.score(X_test, np.array(y_test))

i - 0
i - 1
i - 2
i - 3
i - 4
i - 5
i - 6
i - 7
i - 8
i - 9
i - 10
i - 11
i - 12
i - 13


# plot the relationship between n and accuracy
plt.plot(k_range, test_acc, label = 'Test accuracy')
plt.plot(k_range, train_acc, label = 'Train accuracy')
plt.legend()
plt.xlabel('Number of Neighbors')
plt.ylabel('Accuracy')
plt.show

<function matplotlib.pyplot.show(close=None, block=None)>


# Create Confusion Matrix for testing data using n = 14
locations = list({x.split("_")[0] for x in pleasant.columns})
locations.sort()

key = "_pleasant_weather"
figure, ax = plt.subplots(3, 5, figsize=(25, 15))  
labels = ['unpleasant', 'pleasant']  
count = 0

for i in range(3):
    for j in range(5):
        if count < len(locations):  
            name = locations[count]
            cm = confusion_matrix(y_test.loc[:,name+key], y_pred[:,count])            
            sns.heatmap(cm, annot=True, fmt='g', ax=ax[i, j])
            ax[i, j].set_xlabel('Predicted labels')
            ax[i, j].set_ylabel('True labels')
            ax[i, j].set_title(name + ' Matrix')
            if cm.shape == (2, 2): 
                ax[i, j].xaxis.set_ticklabels(labels)
                ax[i, j].yaxis.set_ticklabels(labels)
            else:  
                unique_labels = [labels[i] for i in np.unique(y_test[name + key])]
                ax[i, j].xaxis.set_ticklabels(unique_labels)
                ax[i, j].yaxis.set_ticklabels(unique_labels)
            count += 1

plt.tight_layout()
plt.show()


# list weather data columns
full.columns.to_list()

['BASEL_cloud_cover',
 'BASEL_wind_speed',
 'BASEL_humidity',
 'BASEL_pressure',
 'BASEL_global_radiation',
 'BASEL_precipitation',
 'BASEL_snow_depth',
 'BASEL_sunshine',
 'BASEL_temp_mean',
 'BASEL_temp_min',
 'BASEL_temp_max',
 'BELGRADE_cloud_cover',
 'BELGRADE_humidity',
 'BELGRADE_pressure',
 'BELGRADE_global_radiation',
 'BELGRADE_precipitation',
 'BELGRADE_sunshine',
 'BELGRADE_temp_mean',
 'BELGRADE_temp_min',
 'BELGRADE_temp_max',
 'BUDAPEST_cloud_cover',
 'BUDAPEST_humidity',
 'BUDAPEST_pressure',
 'BUDAPEST_global_radiation',
 'BUDAPEST_precipitation',
 'BUDAPEST_sunshine',
 'BUDAPEST_temp_mean',
 'BUDAPEST_temp_min',
 'BUDAPEST_temp_max',
 'DEBILT_cloud_cover',
 'DEBILT_wind_speed',
 'DEBILT_humidity',
 'DEBILT_pressure',
 'DEBILT_global_radiation',
 'DEBILT_precipitation',
 'DEBILT_sunshine',
 'DEBILT_temp_mean',
 'DEBILT_temp_min',
 'DEBILT_temp_max',
 'DUSSELDORF_cloud_cover',
 'DUSSELDORF_wind_speed',
 'DUSSELDORF_humidity',
 'DUSSELDORF_pressure',
 'DUSSELDORF_global_radiation',
 'DUSSELDORF_precipitation',
 'DUSSELDORF_snow_depth',
 'DUSSELDORF_sunshine',
 'DUSSELDORF_temp_mean',
 'DUSSELDORF_temp_min',
 'DUSSELDORF_temp_max',
 'GDANSK_cloud_cover',
 'GDANSK_humidity',
 'GDANSK_precipitation',
 'GDANSK_snow_depth',
 'GDANSK_temp_mean',
 'GDANSK_temp_min',
 'GDANSK_temp_max',
 'HEATHROW_cloud_cover',
 'HEATHROW_humidity',
 'HEATHROW_pressure',
 'HEATHROW_global_radiation',
 'HEATHROW_precipitation',
 'HEATHROW_snow_depth',
 'HEATHROW_sunshine',
 'HEATHROW_temp_mean',
 'HEATHROW_temp_min',
 'HEATHROW_temp_max',
 'KASSEL_wind_speed',
 'KASSEL_humidity',
 'KASSEL_pressure',
 'KASSEL_global_radiation',
 'KASSEL_precipitation',
 'KASSEL_sunshine',
 'KASSEL_temp_mean',
 'KASSEL_temp_min',
 'KASSEL_temp_max',
 'LJUBLJANA_cloud_cover',
 'LJUBLJANA_wind_speed',
 'LJUBLJANA_humidity',
 'LJUBLJANA_pressure',
 'LJUBLJANA_global_radiation',
 'LJUBLJANA_precipitation',
 'LJUBLJANA_sunshine',
 'LJUBLJANA_temp_mean',
 'LJUBLJANA_temp_min',
 'LJUBLJANA_temp_max',
 'MAASTRICHT_cloud_cover',
 'MAASTRICHT_wind_speed',
 'MAASTRICHT_humidity',
 'MAASTRICHT_pressure',
 'MAASTRICHT_global_radiation',
 'MAASTRICHT_precipitation',
 'MAASTRICHT_sunshine',
 'MAASTRICHT_temp_mean',
 'MAASTRICHT_temp_min',
 'MAASTRICHT_temp_max',
 'MADRID_cloud_cover',
 'MADRID_wind_speed',
 'MADRID_humidity',
 'MADRID_pressure',
 'MADRID_global_radiation',
 'MADRID_precipitation',
 'MADRID_sunshine',
 'MADRID_temp_mean',
 'MADRID_temp_min',
 'MADRID_temp_max',
 'MUNCHENB_cloud_cover',
 'MUNCHENB_humidity',
 'MUNCHENB_global_radiation',
 'MUNCHENB_precipitation',
 'MUNCHENB_snow_depth',
 'MUNCHENB_sunshine',
 'MUNCHENB_temp_mean',
 'MUNCHENB_temp_min',
 'MUNCHENB_temp_max',
 'OSLO_cloud_cover',
 'OSLO_wind_speed',
 'OSLO_humidity',
 'OSLO_pressure',
 'OSLO_global_radiation',
 'OSLO_precipitation',
 'OSLO_snow_depth',
 'OSLO_sunshine',
 'OSLO_temp_mean',
 'OSLO_temp_min',
 'OSLO_temp_max',
 'ROMA_cloud_cover',
 'ROMA_wind_speed',
 'ROMA_humidity',
 'ROMA_pressure',
 'ROMA_sunshine',
 'ROMA_temp_mean',
 'SONNBLICK_cloud_cover',
 'SONNBLICK_wind_speed',
 'SONNBLICK_humidity',
 'SONNBLICK_pressure',
 'SONNBLICK_global_radiation',
 'SONNBLICK_precipitation',
 'SONNBLICK_sunshine',
 'SONNBLICK_temp_mean',
 'SONNBLICK_temp_min',
 'SONNBLICK_temp_max',
 'STOCKHOLM_cloud_cover',
 'STOCKHOLM_pressure',
 'STOCKHOLM_global_radiation',
 'STOCKHOLM_precipitation',
 'STOCKHOLM_sunshine',
 'STOCKHOLM_temp_mean',
 'STOCKHOLM_temp_min',
 'STOCKHOLM_temp_max',
 'TOURS_wind_speed',
 'TOURS_humidity',
 'TOURS_pressure',
 'TOURS_global_radiation',
 'TOURS_precipitation',
 'TOURS_temp_mean',
 'TOURS_temp_min',
 'TOURS_temp_max',
 'VALENTIA_cloud_cover',
 'VALENTIA_humidity',
 'VALENTIA_pressure',
 'VALENTIA_global_radiation',
 'VALENTIA_precipitation',
 'VALENTIA_snow_depth',
 'VALENTIA_sunshine',
 'VALENTIA_temp_mean',
 'VALENTIA_temp_min',
 'VALENTIA_temp_max',
 'Date',
 'Month']


# remove GDANSK, ROMA, and TOURS entries
full.drop([col for col in full.columns if 'ROMA' in col or 'TOURS' in col or 'GDANSK' in col], axis = 1, inplace = True)
full.shape

(22950, 149)


# drop the Date and Month columns
full.drop(['Date', 'Month'], axis= 1, inplace = True)


full.shape

(22950, 147)


# split data into testing and training sets
X1 = full
y1 = pleasant
X1_train, X1_test, y1_train, y1_test = train_test_split(X1, y1, test_size=0.25, random_state=5)


# convert training and test data to arrays
X1_train = X1_train.values
X1_test = X1_test.values
y1_train = y1_train.values
y1_test = y1_test.values


X1_train.shape

(17212, 147)


y1_train.shape

(17212, 15)


# train and fit the KNN model
k_range = np.arange(1,15)
train_acc1 = np.empty(len(k_range))
test_acc1 = np.empty(len(k_range))
scores1 = {}
scores_list1 = []
for i, k in enumerate(k_range):
    print("i -", i)
    knn = KNeighborsClassifier(n_neighbors=k)
    classifier = MultiOutputClassifier(knn, n_jobs=-1)
    knn.fit(X1_train, y1_train)
    y1_pred_train = knn.predict(X1_train)
    y1_pred = knn.predict(X1_test)
    train_acc1[i] = knn.score(X1_train, np.array(y1_train))
    test_acc1[i] = knn.score(X1_test, np.array(y1_test))

i - 0
i - 1
i - 2
i - 3
i - 4
i - 5
i - 6
i - 7
i - 8
i - 9
i - 10
i - 11
i - 12
i - 13


# plot the relationship between n and accuracy
plt.plot(k_range, test_acc1, label = 'Test accuracy')
plt.plot(k_range, train_acc1, label = 'Train accuracy')
plt.legend()
plt.xlabel('Number of Neighbors')
plt.ylabel('Accuracy')
plt.show
# The low accuracy scores show that the KNN model is underfitting the data
# Will repeat the KNN fit using data from a single location.

<function matplotlib.pyplot.show(close=None, block=None)>


 train_acc1

array([1.        , 0.56937021, 0.56727864, 0.52445968, 0.52875901,
       0.5114455 , 0.51417616, 0.50482222, 0.50522891, 0.49877992,
       0.50087149, 0.49488729, 0.49535208, 0.49244713])


test_acc1

array([0.41321018, 0.43203207, 0.4423144 , 0.44510282, 0.45433949,
       0.45346811, 0.4579993 , 0.46078773, 0.46165911, 0.46218194,
       0.46148484, 0.46287905, 0.46375044, 0.46165911])


# split data into testing and training sets, keep as df
X1 = full
y1 = pleasant
X1_train, X1_test, y1_train, y1_test = train_test_split(X1, y1, test_size=0.25, random_state=5)


# Create Confusion Matrix for testing data using n = 14
locations = list({x.split("_")[0] for x in pleasant.columns})
locations.sort()

key = "_pleasant_weather"
figure, ax = plt.subplots(3, 5, figsize=(25, 15))  
labels = ['unpleasant', 'pleasant']  
count = 0

for i in range(3):
    for j in range(5):
        if count < len(locations):  
            name = locations[count]
            cm = confusion_matrix(y1_test.loc[:,name+key], y1_pred[:,count])            
            sns.heatmap(cm, annot=True, fmt='g', ax=ax[i, j])
            ax[i, j].set_xlabel('Predicted labels')
            ax[i, j].set_ylabel('True labels')
            ax[i, j].set_title(name + ' Matrix')
            if cm.shape == (2, 2): 
                ax[i, j].xaxis.set_ticklabels(labels)
                ax[i, j].yaxis.set_ticklabels(labels)
            else:  
                unique_labels = [labels[i] for i in np.unique(y1_test[name + key])]
                ax[i, j].xaxis.set_ticklabels(unique_labels)
                ax[i, j].yaxis.set_ticklabels(unique_labels)
            count += 1

plt.tight_layout()
plt.show()


# Create Confusion Matrix for training data using n = 14
locations = list({x.split("_")[0] for x in pleasant.columns})
locations.sort()

key = "_pleasant_weather"
figure, ax = plt.subplots(3, 5, figsize=(25, 15))  
labels = ['unpleasant', 'pleasant']  
count = 0

for i in range(3):
    for j in range(5):
        if count < len(locations):  
            name = locations[count]
            cm = confusion_matrix(y1_train.loc[:,name+key], y1_pred_train[:,count])            
            sns.heatmap(cm, annot=True, fmt='g', ax=ax[i, j])
            ax[i, j].set_xlabel('Predicted labels')
            ax[i, j].set_ylabel('True labels')
            ax[i, j].set_title(name + ' Matrix')
            if cm.shape == (2, 2): 
                ax[i, j].xaxis.set_ticklabels(labels)
                ax[i, j].yaxis.set_ticklabels(labels)
            else:  
                unique_labels = [labels[i] for i in np.unique(y1_test[name + key])]
                ax[i, j].xaxis.set_ticklabels(unique_labels)
                ax[i, j].yaxis.set_ticklabels(unique_labels)
            count += 1

plt.tight_layout()
plt.show()


full.head()


# create table with Heathrow data only
Heathrow = full[[col for col in full.columns if 'HEATHROW' in col]]
Heathrow.head()


# create Pleasantness table for Heathrow data only
Heathrow_pleasant = pleasant[['HEATHROW_pleasant_weather']]


Heathrow_pleasant.head()


# split data into testing and training sets
X2 = Heathrow
y2 = Heathrow_pleasant
X2_train, X2_test, y2_train, y2_test = train_test_split(X2, y2, test_size=0.25, random_state=5)


print(X2.shape)
print(y2.shape)
print(X2_train.shape)
print(y2_train.shape)
print(X2_test.shape)
print(y2_test.shape)

(22950, 10)
(22950, 1)
(17212, 10)
(17212, 1)
(5738, 10)
(5738, 1)


# train and fit the KNN model
k_range = np.arange(1,20)
train_acc2 = np.empty(len(k_range))
test_acc2 = np.empty(len(k_range))
scores2 = {}
scores_list2 = []
for i, k in enumerate(k_range):
    print("i -", i)
    knn = KNeighborsClassifier(n_neighbors=k)
    ## Fit the model on the training data.
    knn.fit(X2_train, y2_train)
    y2_pred_train = knn.predict(X2_train)
    y2_pred = knn.predict(X2_test)
    train_acc2[i] = knn.score(X2_train, y2_train)
    test_acc2[i] = knn.score(X2_test, y2_test)

i - 0

C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)

i - 1

C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)

i - 2

C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)

i - 3

C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)

i - 4

C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)


# plot the relationship between n and accuracy
plt.plot(k_range, test_acc2, label = 'Test accuracy')
plt.plot(k_range, train_acc2, label = 'Train accuracy')
plt.legend()
plt.xlabel('Number of Neighbors')
plt.ylabel('Accuracy')
plt.show

# The high accuracy scores show that the KNN model fits this data well.
# Neither over nor under fitting is abserved

<function matplotlib.pyplot.show(close=None, block=None)>


 train_acc2

array([1.        , 0.96583779, 0.96386242, 0.95706484, 0.95567046,
       0.95253312, 0.95439228, 0.95154543, 0.95049965, 0.94910528,
       0.94974436, 0.94869858, 0.94875668, 0.9478271 , 0.9478852 ,
       0.94683941, 0.94590983, 0.94527074, 0.94561934])


 test_acc2

array([0.92349251, 0.91774137, 0.93394911, 0.93098641, 0.93621471,
       0.93760892, 0.93865458, 0.93917741, 0.9407459 , 0.94109446,
       0.9430115 , 0.94353433, 0.94388289, 0.94440572, 0.9452771 ,
       0.94475427, 0.9452771 , 0.94405716, 0.94492855])


# create a confusion matrix for the Heathrow testing data using n = 18
cm = confusion_matrix(y2_test, y2_pred)
plt.figure(figsize=(8, 6))
ax = sns.heatmap(cm, annot=True, fmt="d", linewidths=.5, cmap='Blues')
plt.title('Confusion Matrix for Heathrow')
plt.ylabel('Actual Pleasantness')
plt.xlabel('Predicted Pleasantness')
ax.set_xticklabels(['Unpleasant', 'Pleasant'])
ax.set_yticklabels(['Unpleasant', 'Pleasant'])
plt.show()


# create learning curve to determine best training data size for Heathrow analysis
from sklearn.model_selection import learning_curve

train_sizes, train_scores, validation_scores = learning_curve(
    estimator = KNeighborsClassifier(n_neighbors=5),
    X = X2, y = y2, train_sizes=np.linspace(0.1, 1.0, 10),
    cv=5, scoring='accuracy')

train_scores_mean = np.mean(train_scores, axis=1)
validation_scores_mean = np.mean(validation_scores, axis=1)

plt.plot(train_sizes, train_scores_mean, label='Training score')
plt.plot(train_sizes, validation_scores_mean, label='Validation score')
plt.title('Learning Curve')
plt.xlabel('Training Size')
plt.ylabel('Accuracy Score')
plt.legend(loc="best")
plt.show()

# the training dataset size we used (17212) is appropriate. At this size, 
# the validation score is high without a loss in the training score

C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)
C:\Users\kacie\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:228: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return self._fit(X, y)


# create a pairplot for Heathrow data
Heathrow['HEATHROW_pleasant_weather'] = Heathrow_pleasant['HEATHROW_pleasant_weather']

sns.pairplot(Heathrow[['HEATHROW_cloud_cover',
                       'HEATHROW_humidity',
                       'HEATHROW_pressure',
                       'HEATHROW_global_radiation',
                       'HEATHROW_precipitation',
                       'HEATHROW_snow_depth',
                       'HEATHROW_sunshine',
                       'HEATHROW_temp_mean',
                       'HEATHROW_temp_min',
                       'HEATHROW_temp_max', 
                       'HEATHROW_pleasant_weather']], hue='HEATHROW_pleasant_weather')
plt.show()

C:\Users\kacie\AppData\Local\Temp\ipykernel_21932\4280180493.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  Heathrow['HEATHROW_pleasant_weather'] = Heathrow_pleasant['HEATHROW_pleasant_weather']
C:\Users\kacie\anaconda3\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight
  self._figure.tight_layout(*args, **kwargs)

	BASEL_temp_mean	BELGRADE_temp_mean	BUDAPEST_temp_mean	DEBILT_temp_mean	DUSSELDORF_temp_mean	GDANSK_temp_mean	HEATHROW_temp_mean	KASSEL_temp_mean	LJUBLJANA_temp_mean	MAASTRICHT_temp_mean	MADRID_temp_mean	MUNCHENB_temp_mean	OSLO_temp_mean	ROMA_temp_mean	SONNBLICK_temp_mean	STOCKHOLM_temp_mean	TOURS_temp_mean	VALENTIA_temp_mean	Date
0	-0.528623	-1.016876	-1.099163	-0.114356	-0.105836	-0.927601	-0.106469	-0.182904	-1.370824	-0.097084	-0.988280	-0.265742	-0.186575	-1.280450	-0.124331	-0.391072	-0.257321	-0.668215	19600101
1	-0.582946	-1.107669	-1.110927	-0.367511	-0.370915	-0.825294	-0.892676	-0.212437	-1.043881	-0.232112	-0.691740	-0.353714	-0.368598	-0.539569	-0.650834	-0.415953	-0.335759	-0.548046	19600102
2	-0.257010	-1.084971	-1.063873	-0.509912	-0.532908	-0.940389	-0.490837	-0.389635	-0.741156	-0.487164	-0.853490	-0.403983	-0.550620	-0.876333	-0.650834	-0.615003	-0.210258	-0.067372	19600103
3	-0.555784	-1.209812	-1.146217	-0.525734	-0.577088	-1.042696	-0.316124	-0.493001	-0.910682	-0.472161	-0.624345	-0.642763	-0.417137	-0.775304	-0.943336	-0.764290	-0.069069	-0.998679	19600104
4	-1.003946	-1.209812	-1.087400	-0.320045	-0.444548	-0.978754	-0.403481	-0.552067	-0.862246	-0.307127	-0.381721	-0.906678	-0.332193	-0.926848	-0.621584	-0.503037	-0.037694	-1.509396	19600105

	BASEL_cloud_cover	BASEL_wind_speed	BASEL_humidity	BASEL_pressure	BASEL_global_radiation	BASEL_precipitation	BASEL_snow_depth	BASEL_sunshine	BASEL_temp_mean	BASEL_temp_min	...	VALENTIA_cloud_cover	VALENTIA_humidity	VALENTIA_pressure	VALENTIA_global_radiation	VALENTIA_precipitation	VALENTIA_snow_depth	VALENTIA_sunshine	VALENTIA_temp_mean	VALENTIA_temp_min	VALENTIA_temp_max
0	0.660514	-0.02793	0.826097	-0.001949	-1.101066	-0.265148	-0.179228	-0.902918	-0.528623	-0.845652	...	-0.443701	0.761754	-1.299744	-0.806427	-0.088407	-0.024706	0.372147	-0.668215	-0.519743	-0.752237
1	0.244897	-0.02793	0.735760	-0.001949	-1.058108	1.658760	-0.179228	-0.810126	-0.582946	-0.462450	...	0.783085	1.183580	-1.262455	-1.042055	0.503361	-0.024706	-0.829285	-0.548046	-0.629054	-0.407141
2	1.076130	-0.02793	1.277781	-0.001949	-1.251420	0.155707	-0.179228	-1.065304	-0.257010	-0.186545	...	0.783085	1.183580	-0.432779	-1.136306	-0.396127	-0.024706	-1.009500	-0.067372	0.054135	-0.177078
3	-1.001953	-0.02793	1.458455	-0.001949	-0.821838	-0.445514	-0.179228	-0.114186	-0.555784	-0.385810	...	0.783085	0.480538	0.387574	-1.183432	0.669056	-0.024706	-1.039536	-0.998679	-0.164486	-0.838511
4	0.244897	-0.02793	1.729466	-0.001949	-0.746661	-0.164944	-0.179228	0.187388	-1.003946	-1.075573	...	-1.670486	-0.363113	1.729970	-0.794645	-0.490810	-0.024706	0.672505	-1.509396	-1.339569	-1.471186

	HEATHROW_cloud_cover	HEATHROW_humidity	HEATHROW_pressure	HEATHROW_global_radiation	HEATHROW_precipitation	HEATHROW_snow_depth	HEATHROW_sunshine	HEATHROW_temp_mean	HEATHROW_temp_min	HEATHROW_temp_max
0	0.833387	1.332572	-1.376592	-1.187370	0.141032	-0.073841	-1.064721	-0.106469	0.393470	-1.038492
1	0.833387	2.004870	-0.984359	-1.187370	0.167279	-0.073841	-1.064721	-0.892676	-0.639866	-0.687774
2	1.324799	1.812785	0.115808	-1.164817	-0.252671	-0.073841	-1.039671	-0.490837	-0.226532	-0.443796
3	1.324799	2.004870	0.728074	-1.187370	-0.436400	-0.073841	-1.064721	-0.316124	-0.113804	-0.947000
4	-0.149436	0.660275	1.158574	-0.995669	-0.436400	-0.073841	-0.538678	-0.403481	0.299530	-1.206227

Climate Data K-Nearest Neighbors Analysis¶

Contents¶

KNN fit to average temperature data¶

KNN fit to full data¶

KNN fit to Heathrow data¶

KNN fit to average temperature data¶

KNN fit to full data¶

KNN fit to Heathrow data¶

	DATE	BASEL_pleasant_weather	BELGRADE_pleasant_weather	BUDAPEST_pleasant_weather	DEBILT_pleasant_weather	DUSSELDORF_pleasant_weather	HEATHROW_pleasant_weather	KASSEL_pleasant_weather	LJUBLJANA_pleasant_weather	MAASTRICHT_pleasant_weather	MADRID_pleasant_weather	MUNCHENB_pleasant_weather	OSLO_pleasant_weather	SONNBLICK_pleasant_weather	STOCKHOLM_pleasant_weather	VALENTIA_pleasant_weather
0	19600101	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1	19600102	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2	19600103	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
3	19600104	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
4	19600105	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

	HEATHROW_pleasant_weather
0	0
1	0
2	0
3	0
4	0